swapping to local embeddings

backend/app/api/v1/rag.py

@@ -7,6 +7,7 @@ from typing import List, Optional, Dict, Any
 from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, status
 from fastapi.responses import StreamingResponse
 from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
 from pydantic import BaseModel
 import io
 import asyncio
@@ -15,6 +16,7 @@ from datetime import datetime
 from app.db.database import get_db
 from app.core.security import get_current_user
 from app.models.user import User
+from app.models.rag_collection import RagCollection
 from app.services.rag_service import RAGService
 from app.utils.exceptions import APIException
 
@@ -268,7 +270,19 @@ async def get_documents(
                 try:
                     collection_id_int = int(collection_id)
                 except (ValueError, TypeError):
-                    raise HTTPException(status_code=400, detail="Invalid collection_id format")
+                    # Attempt to resolve by Qdrant collection name
+                    collection_row = await db.scalar(
+                        select(RagCollection).where(RagCollection.qdrant_collection_name == collection_id)
+                    )
+                    if collection_row:
+                        collection_id_int = collection_row.id
+                    else:
+                        # Unknown collection identifier; return empty result instead of erroring out
+                        return {
+                            "success": True,
+                            "documents": [],
+                            "total": 0
+                        }
         
         rag_service = RAGService(db)
         documents = await rag_service.get_documents(

backend/app/core/config.py

@@ -129,6 +129,7 @@ class Settings(BaseSettings):
     RAG_EMBEDDING_DELAY_PER_REQUEST: float = float(os.getenv("RAG_EMBEDDING_DELAY_PER_REQUEST", "0.5"))
     RAG_ALLOW_FALLBACK_EMBEDDINGS: bool = os.getenv("RAG_ALLOW_FALLBACK_EMBEDDINGS", "True").lower() == "true"
     RAG_WARN_ON_FALLBACK: bool = os.getenv("RAG_WARN_ON_FALLBACK", "True").lower() == "true"
+    RAG_EMBEDDING_MODEL: str = os.getenv("RAG_EMBEDDING_MODEL", "BAAI/bge-small-en")
     RAG_DOCUMENT_PROCESSING_TIMEOUT: int = int(os.getenv("RAG_DOCUMENT_PROCESSING_TIMEOUT", "300"))
     RAG_EMBEDDING_GENERATION_TIMEOUT: int = int(os.getenv("RAG_EMBEDDING_GENERATION_TIMEOUT", "120"))
     RAG_INDEXING_TIMEOUT: int = int(os.getenv("RAG_INDEXING_TIMEOUT", "120"))
@@ -154,4 +155,3 @@ class Settings(BaseSettings):
 
 # Global settings instance
 settings = Settings()
-

backend/app/modules/rag/main.py

@@ -8,6 +8,7 @@ import json
 import logging
 import mimetypes
 import re
+import time
 from typing import Any, Dict, List, Optional, Tuple, Union
 from datetime import datetime
 from dataclasses import dataclass, asdict
@@ -146,6 +147,11 @@ class RAGModule(BaseModule):
         # Update with any provided config
         if config:
             self.config.update(config)
+
+        # Ensure embedding model configured (defaults to local BGE small)
+        default_embedding_model = getattr(settings, 'RAG_EMBEDDING_MODEL', 'BAAI/bge-small-en')
+        self.config.setdefault("embedding_model", default_embedding_model)
+        self.default_embedding_model = default_embedding_model
         
         # Content processing components
         self.nlp_model = None
@@ -178,6 +184,7 @@ class RAGModule(BaseModule):
             "supported_types": len(self.supported_types)
         }
         self.search_cache = {}
+        self.collection_vector_sizes: Dict[str, int] = {}
     
     def get_required_permissions(self) -> List[Permission]:
         """Return list of permissions this module requires"""
@@ -215,7 +222,7 @@ class RAGModule(BaseModule):
             self.initialized = True
             log_module_event("rag", "initialized", {
                 "vector_db": self.config.get("vector_db", "qdrant"),
-                "embedding_model": self.embedding_model.get("model_name", "intfloat/multilingual-e5-large-instruct"),
+                "embedding_model": self.embedding_model.get("model_name", self.default_embedding_model),
                 "chunk_size": self.config.get("chunk_size", 400),
                 "max_results": self.config.get("max_results", 10),
                 "supported_file_types": list(self.supported_types.keys()),
@@ -427,8 +434,7 @@ class RAGModule(BaseModule):
         # Prefer enhanced embedding service (rate limiting + retry)
         from app.services.enhanced_embedding_service import enhanced_embedding_service as embedding_service
         
-        # Use intfloat/multilingual-e5-large-instruct for LLM service integration
-        model_name = self.config.get("embedding_model", "intfloat/multilingual-e5-large-instruct")
+        model_name = self.config.get("embedding_model", self.default_embedding_model)
         embedding_service.model_name = model_name
         
         # Initialize the embedding service
@@ -439,7 +445,7 @@ class RAGModule(BaseModule):
             logger.info(f"Successfully initialized embedding service with {model_name}")
             return {
                 "model_name": model_name,
-                "dimension": embedding_service.dimension or 768
+                "dimension": embedding_service.dimension or 384
             }
         else:
             # Fallback to mock implementation
@@ -447,7 +453,7 @@ class RAGModule(BaseModule):
             self.embedding_service = None
             return {
                 "model_name": model_name,
-                "dimension": 1024  # Default dimension for intfloat/multilingual-e5-large-instruct
+                "dimension": 384  # Default dimension matching local bge-small embeddings
             }
     
     async def _initialize_content_processing(self):
@@ -588,16 +594,37 @@ class RAGModule(BaseModule):
             collection_names = await self._get_collections_safely()
             
             if collection_name not in collection_names:
-                # Create collection
+                # Create collection with the current embedding dimension
+                vector_dimension = self.embedding_model.get(
+                    "dimension",
+                    getattr(self.embedding_service, "dimension", 384) or 384
+                )
+
                 self.qdrant_client.create_collection(
                     collection_name=collection_name,
                     vectors_config=VectorParams(
-                        size=self.embedding_model.get("dimension", 768),
+                        size=vector_dimension,
                         distance=Distance.COSINE
                     )
                 )
+                self.collection_vector_sizes[collection_name] = vector_dimension
                 log_module_event("rag", "collection_created", {"collection": collection_name})
-            
+            else:
+                # Cache existing collection vector size for later alignment
+                try:
+                    info = self.qdrant_client.get_collection(collection_name)
+                    vectors_param = getattr(info.config.params, "vectors", None) if hasattr(info, "config") else None
+                    existing_size = None
+                    if vectors_param is not None and hasattr(vectors_param, "size"):
+                        existing_size = vectors_param.size
+                    elif isinstance(vectors_param, dict):
+                        existing_size = vectors_param.get("size")
+
+                    if existing_size:
+                        self.collection_vector_sizes[collection_name] = existing_size
+                except Exception as inner_error:
+                    logger.debug(f"Unable to cache vector size for collection {collection_name}: {inner_error}")
+
         except Exception as e:
             logger.error(f"Error ensuring collection exists: {e}")
             raise
@@ -632,12 +659,13 @@ class RAGModule(BaseModule):
     async def _generate_embedding(self, text: str) -> List[float]:
         """Generate embedding for text"""
         if self.embedding_service:
-            # Use real embedding service
-            return await self.embedding_service.get_embedding(text)
+            vector = await self.embedding_service.get_embedding(text)
+            return vector
         else:
             # Fallback to deterministic random embedding for consistency
             np.random.seed(hash(text) % 2**32)
-            return np.random.random(self.embedding_model.get("dimension", 768)).tolist()
+            fallback_dim = self.embedding_model.get("dimension", getattr(self.embedding_service, "dimension", 384) or 384)
+            return np.random.random(fallback_dim).tolist()
     
     async def _generate_embeddings(self, texts: List[str], is_document: bool = True) -> List[List[float]]:
         """Generate embeddings for multiple texts (batch processing)"""
@@ -651,14 +679,90 @@ class RAGModule(BaseModule):
                 prefixed_texts = texts
 
             # Use real embedding service for batch processing
-            return await self.embedding_service.get_embeddings(prefixed_texts)
+            backend = getattr(self.embedding_service, "backend", "unknown")
+            start_time = time.time()
+            logger.info(
+                "Embedding batch requested",
+                extra={
+                    "backend": backend,
+                    "model": getattr(self.embedding_service, "model_name", "unknown"),
+                    "count": len(prefixed_texts),
+                    "scope": "documents" if is_document else "queries"
+                },
+            )
+            embeddings = await self.embedding_service.get_embeddings(prefixed_texts)
+            duration = time.time() - start_time
+            logger.info(
+                "Embedding batch finished",
+                extra={
+                    "backend": backend,
+                    "model": getattr(self.embedding_service, "model_name", "unknown"),
+                    "count": len(embeddings),
+                    "scope": "documents" if is_document else "queries",
+                    "duration_sec": round(duration, 4)
+                },
+            )
+            return embeddings
         else:
             # Fallback to individual processing
+            logger.warning(
+                "Embedding service unavailable, falling back to per-item generation",
+                extra={
+                    "count": len(texts),
+                    "scope": "documents" if is_document else "queries"
+                },
+            )
             embeddings = []
             for text in texts:
                 embedding = await self._generate_embedding(text)
                 embeddings.append(embedding)
             return embeddings
+
+    def _get_collection_vector_size(self, collection_name: Optional[str]) -> int:
+        """Return the expected vector size for a collection, caching results."""
+        default_dim = self.embedding_model.get(
+            "dimension",
+            getattr(self.embedding_service, "dimension", 384) or 384
+        )
+
+        if not collection_name:
+            return default_dim
+
+        if collection_name in self.collection_vector_sizes:
+            return self.collection_vector_sizes[collection_name]
+
+        try:
+            info = self.qdrant_client.get_collection(collection_name)
+            vectors_param = getattr(info.config.params, "vectors", None) if hasattr(info, "config") else None
+            existing_size = None
+            if vectors_param is not None and hasattr(vectors_param, "size"):
+                existing_size = vectors_param.size
+            elif isinstance(vectors_param, dict):
+                existing_size = vectors_param.get("size")
+
+            if existing_size:
+                self.collection_vector_sizes[collection_name] = existing_size
+                return existing_size
+        except Exception as e:
+            logger.debug(f"Unable to determine vector size for {collection_name}: {e}")
+
+        self.collection_vector_sizes[collection_name] = default_dim
+        return default_dim
+
+    def _align_embedding_dimension(self, vector: List[float], collection_name: Optional[str]) -> List[float]:
+        """Pad or truncate embeddings to match the target collection dimension."""
+        if vector is None:
+            return vector
+
+        target_dim = self._get_collection_vector_size(collection_name)
+        current_dim = len(vector)
+
+        if current_dim == target_dim:
+            return vector
+        if current_dim > target_dim:
+            return vector[:target_dim]
+        padding = [0.0] * (target_dim - current_dim)
+        return vector + padding
     
     def _chunk_text(self, text: str, chunk_size: int = None) -> List[str]:
         """Split text into overlapping chunks for better context preservation"""
@@ -1176,6 +1280,7 @@ class RAGModule(BaseModule):
             # Create document points
             points = []
             for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+                aligned_embedding = self._align_embedding_dimension(embedding, collection_name)
                 chunk_id = str(uuid.uuid4())
                 
                 chunk_metadata = {
@@ -1189,7 +1294,7 @@ class RAGModule(BaseModule):
                 
                 points.append(PointStruct(
                     id=chunk_id,
-                    vector=embedding,
+                    vector=aligned_embedding,
                     payload=chunk_metadata
                 ))
             
@@ -1257,6 +1362,7 @@ class RAGModule(BaseModule):
             # Create document points with enhanced metadata
             points = []
             for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+                aligned_embedding = self._align_embedding_dimension(embedding, collection_name)
                 chunk_id = str(uuid.uuid4())
                 
                 chunk_metadata = {
@@ -1280,7 +1386,7 @@ class RAGModule(BaseModule):
                 
                 points.append(PointStruct(
                     id=chunk_id,
-                    vector=embedding,
+                    vector=aligned_embedding,
                     payload=chunk_metadata
                 ))
             
@@ -1514,9 +1620,9 @@ class RAGModule(BaseModule):
             start_time = time.time()
             
             # Generate query embedding with task-specific prefix for better retrieval
-            # The E5 model works better with "query:" prefix for search queries
             optimized_query = f"query: {query}"
             query_embedding = await self._generate_embedding(optimized_query)
+            query_embedding = self._align_embedding_dimension(query_embedding, collection_name)
             
             # Build filter
             search_filter = None

backend/app/services/embedding_service.py

@@ -1,148 +1,133 @@
 """
 Embedding Service
-Provides text embedding functionality using LLM service
+Provides local sentence-transformer embeddings (default: BAAI/bge-small-en).
+Falls back to deterministic random vectors when the local model is unavailable.
 """
 
+import asyncio
 import logging
+import time
 from typing import List, Dict, Any, Optional
 import numpy as np
 
+from app.core.config import settings
+
 logger = logging.getLogger(__name__)
 
 
 class EmbeddingService:
-    """Service for generating text embeddings using LLM service"""
-    
-    def __init__(self, model_name: str = "intfloat/multilingual-e5-large-instruct"):
-        self.model_name = model_name
-        self.dimension = 1024  # Actual dimension for intfloat/multilingual-e5-large-instruct
+    """Service for generating text embeddings using a local transformer model"""
+
+    def __init__(self, model_name: Optional[str] = None):
+        self.model_name = model_name or getattr(settings, "RAG_EMBEDDING_MODEL", "BAAI/bge-small-en")
+        self.dimension = 384  # bge-small produces 384-d vectors
         self.initialized = False
-        
+        self.local_model = None
+        self.backend = "uninitialized"
+
     async def initialize(self):
         """Initialize the embedding service with LLM service"""
         try:
-            from app.services.llm.service import llm_service
-            
-            # Initialize LLM service if not already done
-            if not llm_service._initialized:
-                await llm_service.initialize()
-            
-            # Test LLM service health
-            if not llm_service._initialized:
-                logger.error("LLM service not initialized")
-                return False
+            from sentence_transformers import SentenceTransformer
 
-            # Check if PrivateMode provider is available
-            try:
-                provider_status = await llm_service.get_provider_status()
-                privatemode_status = provider_status.get("privatemode")
-                if not privatemode_status or privatemode_status.status != "healthy":
-                    logger.error(f"PrivateMode provider not available: {privatemode_status}")
-                    return False
-            except Exception as e:
-                logger.error(f"Failed to check provider status: {e}")
-                return False
-            
+            loop = asyncio.get_running_loop()
+
+            def load_model():
+                # Load model synchronously in a worker thread to avoid blocking event loop
+                return SentenceTransformer(self.model_name)
+
+            self.local_model = await loop.run_in_executor(None, load_model)
+            self.dimension = self.local_model.get_sentence_embedding_dimension()
             self.initialized = True
-            logger.info(f"Embedding service initialized with LLM service: {self.model_name} (dimension: {self.dimension})")
+            self.backend = "sentence_transformer"
+            logger.info(
+                "Embedding service initialized with local model %s (dimension: %s)",
+                self.model_name,
+                self.dimension,
+            )
             return True
-            
-        except Exception as e:
-            logger.error(f"Failed to initialize LLM embedding service: {e}")
-            logger.warning("Using fallback random embeddings")
+
+        except ImportError as exc:
+            logger.error("sentence-transformers not installed: %s", exc)
+            logger.warning("Falling back to random embeddings")
+            self.local_model = None
+            self.initialized = False
+            self.backend = "fallback_random"
+            return False
+
+        except Exception as exc:
+            logger.error(f"Failed to load local embedding model {self.model_name}: {exc}")
+            logger.warning("Falling back to random embeddings")
+            self.local_model = None
+            self.initialized = False
+            self.backend = "fallback_random"
             return False
     
     async def get_embedding(self, text: str) -> List[float]:
         """Get embedding for a single text"""
         embeddings = await self.get_embeddings([text])
         return embeddings[0]
-    
+
     async def get_embeddings(self, texts: List[str]) -> List[List[float]]:
         """Get embeddings for multiple texts using LLM service"""
-        if not self.initialized:
-            # Fallback to random embeddings if not initialized
-            logger.warning("LLM service not available, using random embeddings")
-            return self._generate_fallback_embeddings(texts)
-        
-        try:
-            embeddings = []
-            # Process texts in batches for efficiency
-            batch_size = 10
-            for i in range(0, len(texts), batch_size):
-                batch = texts[i:i+batch_size]
-                
-                # Process each text in the batch
-                batch_embeddings = []
-                for text in batch:
-                    try:
-                        # Truncate text if it's too long for the model's context window
-                        # intfloat/multilingual-e5-large-instruct has a 512 token limit, truncate to ~400 tokens worth of chars
-                        # Rough estimate: 1 token ≈ 4 characters, so 400 tokens ≈ 1600 chars
-                        max_chars = 1600
-                        if len(text) > max_chars:
-                            truncated_text = text[:max_chars]
-                            logger.debug(f"Truncated text from {len(text)} to {max_chars} chars for embedding")
-                        else:
-                            truncated_text = text
-                        
-                        # Guard: skip empty inputs (validator rejects empty strings)
-                        if not truncated_text.strip():
-                            logger.debug("Empty input for embedding; using fallback vector")
-                            batch_embeddings.append(self._generate_fallback_embedding(text))
-                            continue
+        start_time = time.time()
 
-                        # Call LLM service embedding endpoint
-                        from app.services.llm.service import llm_service
-                        from app.services.llm.models import EmbeddingRequest
-                        
-                        llm_request = EmbeddingRequest(
-                            model=self.model_name,
-                            input=truncated_text,
-                            user_id="rag_system",
-                            api_key_id=0  # System API key
-                        )
-                        
-                        response = await llm_service.create_embedding(llm_request)
-                        
-                        # Extract embedding from response
-                        if response.data and len(response.data) > 0:
-                            embedding = response.data[0].embedding
-                            if embedding:
-                                batch_embeddings.append(embedding)
-                                # Update dimension based on actual embedding size
-                                if not hasattr(self, '_dimension_confirmed'):
-                                    self.dimension = len(embedding)
-                                    self._dimension_confirmed = True
-                                    logger.info(f"Confirmed embedding dimension: {self.dimension}")
-                            else:
-                                logger.warning(f"No embedding in response for text: {text[:50]}...")
-                                batch_embeddings.append(self._generate_fallback_embedding(text))
-                        else:
-                            logger.warning(f"Invalid response structure for text: {text[:50]}...")
-                            batch_embeddings.append(self._generate_fallback_embedding(text))
-                    except Exception as e:
-                        logger.error(f"Error getting embedding for text: {e}")
-                        batch_embeddings.append(self._generate_fallback_embedding(text))
-                
-                embeddings.extend(batch_embeddings)
-            
-            return embeddings
-            
-        except Exception as e:
-            logger.error(f"Error generating embeddings with LLM service: {e}")
-            # Fallback to random embeddings
-            return self._generate_fallback_embeddings(texts)
+        if self.local_model:
+            if not texts:
+                return []
+
+            loop = asyncio.get_running_loop()
+
+            try:
+                embeddings = await loop.run_in_executor(
+                    None,
+                    lambda: self.local_model.encode(
+                        texts,
+                        convert_to_numpy=True,
+                        normalize_embeddings=True,
+                    ),
+                )
+                duration = time.time() - start_time
+                logger.info(
+                    "Embedding batch completed",
+                    extra={
+                        "backend": self.backend,
+                        "model": self.model_name,
+                        "count": len(texts),
+                        "dimension": self.dimension,
+                        "duration_sec": round(duration, 4),
+                    },
+                )
+                return embeddings.tolist()
+            except Exception as exc:
+                logger.error(f"Local embedding generation failed: {exc}")
+                self.backend = "fallback_random"
+                return self._generate_fallback_embeddings(texts, duration=time.time() - start_time)
+
+        logger.warning("Local embedding model unavailable; using fallback random embeddings")
+        self.backend = "fallback_random"
+        return self._generate_fallback_embeddings(texts, duration=time.time() - start_time)
     
-    def _generate_fallback_embeddings(self, texts: List[str]) -> List[List[float]]:
+    def _generate_fallback_embeddings(self, texts: List[str], duration: float = None) -> List[List[float]]:
         """Generate fallback random embeddings when model unavailable"""
         embeddings = []
         for text in texts:
             embeddings.append(self._generate_fallback_embedding(text))
+        logger.info(
+            "Embedding batch completed",
+            extra={
+                "backend": "fallback_random",
+                "model": self.model_name,
+                "count": len(texts),
+                "dimension": self.dimension,
+                "duration_sec": round(duration or 0.0, 4),
+            },
+        )
         return embeddings
     
     def _generate_fallback_embedding(self, text: str) -> List[float]:
         """Generate a single fallback embedding"""
-        dimension = self.dimension or 1024  # Default dimension for intfloat/multilingual-e5-large-instruct
+        dimension = self.dimension or 384
         # Use hash for reproducible random embeddings
         np.random.seed(hash(text) % 2**32)
         return np.random.random(dimension).tolist()
@@ -169,22 +154,15 @@ class EmbeddingService:
             "model_name": self.model_name,
             "model_loaded": self.initialized,
             "dimension": self.dimension,
-            "backend": "LLM Service",
+            "backend": self.backend,
             "initialized": self.initialized
         }
-    
+
     async def cleanup(self):
         """Cleanup resources"""
-        # Cleanup LLM service to prevent memory leaks
-        try:
-            from .llm.service import llm_service
-            if llm_service._initialized:
-                await llm_service.cleanup()
-                logger.info("Cleaned up LLM service from embedding service")
-        except Exception as e:
-            logger.error(f"Error cleaning up LLM service: {e}")
-
+        self.local_model = None
         self.initialized = False
+        self.backend = "uninitialized"
 
 
 # Global embedding service instance

backend/app/services/enhanced_embedding_service.py

@@ -1,14 +1,13 @@
 # Enhanced Embedding Service with Rate Limiting Handling
 """
-Enhanced embedding service with robust rate limiting and retry logic
+Enhanced embedding service that adds basic retry semantics around the local
+embedding generator. Since embeddings are fully local, rate-limiting metadata is
+largely informational but retained for compatibility.
 """
 
-import asyncio
 import logging
 import time
-from typing import List, Dict, Any, Optional
-import numpy as np
-from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional
 
 from .embedding_service import EmbeddingService
 from app.core.config import settings
@@ -17,9 +16,9 @@ logger = logging.getLogger(__name__)
 
 
 class EnhancedEmbeddingService(EmbeddingService):
-    """Enhanced embedding service with rate limiting handling"""
+    """Enhanced embedding service with lightweight retry bookkeeping"""
 
-    def __init__(self, model_name: str = "intfloat/multilingual-e5-large-instruct"):
+    def __init__(self, model_name: Optional[str] = None):
         super().__init__(model_name)
         self.rate_limit_tracker = {
             'requests_count': 0,
@@ -34,161 +33,16 @@ class EnhancedEmbeddingService(EmbeddingService):
 
     async def get_embeddings_with_retry(self, texts: List[str], max_retries: int = None) -> tuple[List[List[float]], bool]:
         """
-        Get embeddings with rate limiting and retry logic
+        Get embeddings with retry bookkeeping.
         """
-        if max_retries is None:
-            max_retries = int(getattr(settings, 'RAG_EMBEDDING_RETRY_COUNT', 3))
-
-        batch_size = int(getattr(settings, 'RAG_EMBEDDING_BATCH_SIZE', 3))
-
-        if not self.initialized:
-            logger.warning("Embedding service not initialized, using fallback")
-            return self._generate_fallback_embeddings(texts), False
-
-        embeddings = []
-        success = True
-
-        for i in range(0, len(texts), batch_size):
-            batch = texts[i:i+batch_size]
-            batch_embeddings, batch_success = await self._get_batch_embeddings_with_retry(batch, max_retries)
-            embeddings.extend(batch_embeddings)
-            success = success and batch_success
-
-            # Add delay between batches to avoid rate limiting
-            if i + batch_size < len(texts):
-                delay = self.rate_limit_tracker['delay_between_batches']
-                await asyncio.sleep(delay)  # Configurable delay between batches
-
-        return embeddings, success
-
-    async def _get_batch_embeddings_with_retry(self, texts: List[str], max_retries: int) -> tuple[List[List[float]], bool]:
-        """Get embeddings for a batch with retry logic"""
-        last_error = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                # Check rate limit before making request
-                if self._is_rate_limited():
-                    delay = self._get_rate_limit_delay()
-                    logger.warning(f"Rate limit detected, waiting {delay} seconds")
-                    await asyncio.sleep(delay)
-                    continue
-
-                # Make the request
-                embeddings = await self._get_embeddings_batch_impl(texts)
-
-                return embeddings, True
-
-            except Exception as e:
-                last_error = e
-                error_msg = str(e).lower()
-
-                # Check if it's a rate limit error
-                if any(indicator in error_msg for indicator in ['429', 'rate limit', 'too many requests', 'quota exceeded']):
-                    logger.warning(f"Rate limit error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                    self._update_rate_limit_tracker(success=False)
-
-                    if attempt < max_retries:
-                        delay = self.rate_limit_tracker['retry_delays'][min(attempt, len(self.rate_limit_tracker['retry_delays']) - 1)]
-                        logger.info(f"Retrying in {delay} seconds...")
-                        await asyncio.sleep(delay)
-                        continue
-                    else:
-                        logger.error(f"Max retries exceeded for rate limit, using fallback embeddings")
-                        return self._generate_fallback_embeddings(texts), False
-                else:
-                    # Non-rate-limit error
-                    logger.error(f"Error generating embeddings: {e}")
-                    if attempt < max_retries:
-                        delay = self.rate_limit_tracker['retry_delays'][min(attempt, len(self.rate_limit_tracker['retry_delays']) - 1)]
-                        await asyncio.sleep(delay)
-                    else:
-                        logger.error("Max retries exceeded, using fallback embeddings")
-                        return self._generate_fallback_embeddings(texts), False
-
-        # If we get here, all retries failed
-        logger.error(f"All retries failed, last error: {last_error}")
-        return self._generate_fallback_embeddings(texts), False
-
-    async def _get_embeddings_batch_impl(self, texts: List[str]) -> List[List[float]]:
-        """Implementation of getting embeddings for a batch"""
-        from app.services.llm.service import llm_service
-        from app.services.llm.models import EmbeddingRequest
-
-        embeddings = []
-
-        for text in texts:
-            # Respect rate limit before each request
-            while self._is_rate_limited():
-                delay = self._get_rate_limit_delay()
-                logger.warning(f"Rate limit window exceeded, waiting {delay:.2f}s before next request")
-                await asyncio.sleep(delay)
-
-            # Truncate text if needed
-            max_chars = 1600
-            truncated_text = text[:max_chars] if len(text) > max_chars else text
-
-            llm_request = EmbeddingRequest(
-                model=self.model_name,
-                input=truncated_text,
-                user_id="rag_system",
-                api_key_id=0
+        embeddings = await super().get_embeddings(texts)
+        success = self.local_model is not None
+        if not success:
+            logger.warning(
+                "Embedding service operating in fallback mode; consider installing the local model %s",
+                self.model_name,
             )
-
-            response = await llm_service.create_embedding(llm_request)
-
-            if response.data and len(response.data) > 0:
-                embedding = response.data[0].embedding
-                if embedding:
-                    embeddings.append(embedding)
-                    if not hasattr(self, '_dimension_confirmed'):
-                        self.dimension = len(embedding)
-                        self._dimension_confirmed = True
-                else:
-                    raise ValueError("Empty embedding in response")
-            else:
-                raise ValueError("Invalid response structure")
-
-            # Count this successful request and optionally delay between requests
-            self._update_rate_limit_tracker(success=True)
-            per_req_delay = self.rate_limit_tracker.get('delay_per_request', 0)
-            if per_req_delay and per_req_delay > 0:
-                await asyncio.sleep(per_req_delay)
-
-        return embeddings
-
-    def _is_rate_limited(self) -> bool:
-        """Check if we're currently rate limited"""
-        now = time.time()
-        window_start = self.rate_limit_tracker['window_start']
-
-        # Reset window if it's expired
-        if now - window_start > self.rate_limit_tracker['window_size']:
-            self.rate_limit_tracker['requests_count'] = 0
-            self.rate_limit_tracker['window_start'] = now
-            return False
-
-        # Check if we've exceeded the limit
-        return self.rate_limit_tracker['requests_count'] >= self.rate_limit_tracker['max_requests_per_minute']
-
-    def _get_rate_limit_delay(self) -> float:
-        """Get delay to wait for rate limit reset"""
-        now = time.time()
-        window_end = self.rate_limit_tracker['window_start'] + self.rate_limit_tracker['window_size']
-        return max(0, window_end - now)
-
-    def _update_rate_limit_tracker(self, success: bool):
-        """Update the rate limit tracker"""
-        now = time.time()
-
-        # Reset window if it's expired
-        if now - self.rate_limit_tracker['window_start'] > self.rate_limit_tracker['window_size']:
-            self.rate_limit_tracker['requests_count'] = 0
-            self.rate_limit_tracker['window_start'] = now
-
-        # Increment counter on successful requests
-        if success:
-            self.rate_limit_tracker['requests_count'] += 1
+        return embeddings, success
 
     async def get_embedding_stats(self) -> Dict[str, Any]:
         """Get embedding service statistics including rate limiting info"""

backend/app/services/rag_service.py

@@ -566,10 +566,14 @@ class RAGService:
                 logger.warning(f"Could not check existing collections: {e}")
                 
             # Create collection with proper vector configuration  
+            from app.services.embedding_service import embedding_service
+
+            vector_dimension = getattr(embedding_service, 'dimension', 384) or 384
+
             client.create_collection(
                 collection_name=collection_name,
                 vectors_config=VectorParams(
-                    size=1024,  # Updated for multilingual-e5-large-instruct model
+                    size=vector_dimension,
                     distance=Distance.COSINE
                 ),
                 optimizers_config=models.OptimizersConfigDiff(

backend/modules/rag/main.py

@@ -8,6 +8,7 @@ import json
 import logging
 import mimetypes
 import re
+import time
 from typing import Any, Dict, List, Optional, Tuple, Union
 from datetime import datetime
 from dataclasses import dataclass, asdict
@@ -146,6 +147,11 @@ class RAGModule(BaseModule):
         # Update with any provided config
         if config:
             self.config.update(config)
+
+        # Ensure embedding model configured (defaults to local BGE small)
+        default_embedding_model = getattr(settings, 'RAG_EMBEDDING_MODEL', 'BAAI/bge-small-en')
+        self.config.setdefault("embedding_model", default_embedding_model)
+        self.default_embedding_model = default_embedding_model
         
         # Content processing components
         self.nlp_model = None
@@ -178,6 +184,7 @@ class RAGModule(BaseModule):
             "supported_types": len(self.supported_types)
         }
         self.search_cache = {}
+        self.collection_vector_sizes: Dict[str, int] = {}
     
     def get_required_permissions(self) -> List[Permission]:
         """Return list of permissions this module requires"""
@@ -215,7 +222,7 @@ class RAGModule(BaseModule):
             self.initialized = True
             log_module_event("rag", "initialized", {
                 "vector_db": self.config.get("vector_db", "qdrant"),
-                "embedding_model": self.embedding_model.get("model_name", "intfloat/multilingual-e5-large-instruct"),
+                "embedding_model": self.embedding_model.get("model_name", self.default_embedding_model),
                 "chunk_size": self.config.get("chunk_size", 400),
                 "max_results": self.config.get("max_results", 10),
                 "supported_file_types": list(self.supported_types.keys()),
@@ -426,8 +433,7 @@ class RAGModule(BaseModule):
         """Initialize embedding model"""
         from app.services.embedding_service import embedding_service
         
-        # Use intfloat/multilingual-e5-large-instruct for LLM service integration
-        model_name = self.config.get("embedding_model", "intfloat/multilingual-e5-large-instruct")
+        model_name = self.config.get("embedding_model", self.default_embedding_model)
         embedding_service.model_name = model_name
         
         # Initialize the embedding service
@@ -438,7 +444,7 @@ class RAGModule(BaseModule):
             logger.info(f"Successfully initialized embedding service with {model_name}")
             return {
                 "model_name": model_name,
-                "dimension": embedding_service.dimension or 768
+                "dimension": embedding_service.dimension or 384
             }
         else:
             # Fallback to mock implementation
@@ -446,7 +452,7 @@ class RAGModule(BaseModule):
             self.embedding_service = None
             return {
                 "model_name": model_name,
-                "dimension": 1024  # Default dimension for intfloat/multilingual-e5-large-instruct
+                "dimension": 384  # Default dimension matching local bge-small embeddings
             }
     
     async def _initialize_content_processing(self):
@@ -585,18 +591,39 @@ class RAGModule(BaseModule):
         try:
             # Use safe collection fetching to avoid Pydantic validation errors
             collection_names = await self._get_collections_safely()
-            
+
             if collection_name not in collection_names:
-                # Create collection
+                # Create collection with the current embedding dimension
+                vector_dimension = self.embedding_model.get(
+                    "dimension",
+                    getattr(self.embedding_service, "dimension", 384) or 384
+                )
+
                 self.qdrant_client.create_collection(
                     collection_name=collection_name,
                     vectors_config=VectorParams(
-                        size=self.embedding_model.get("dimension", 768),
+                        size=vector_dimension,
                         distance=Distance.COSINE
                     )
                 )
+                self.collection_vector_sizes[collection_name] = vector_dimension
                 log_module_event("rag", "collection_created", {"collection": collection_name})
-            
+            else:
+                # Cache existing collection vector size for later alignment
+                try:
+                    info = self.qdrant_client.get_collection(collection_name)
+                    vectors_param = getattr(info.config.params, "vectors", None) if hasattr(info, "config") else None
+                    existing_size = None
+                    if vectors_param is not None and hasattr(vectors_param, "size"):
+                        existing_size = vectors_param.size
+                    elif isinstance(vectors_param, dict):
+                        existing_size = vectors_param.get("size")
+
+                    if existing_size:
+                        self.collection_vector_sizes[collection_name] = existing_size
+                except Exception as inner_error:
+                    logger.debug(f"Unable to cache vector size for collection {collection_name}: {inner_error}")
+
         except Exception as e:
             logger.error(f"Error ensuring collection exists: {e}")
             raise
@@ -632,11 +659,13 @@ class RAGModule(BaseModule):
         """Generate embedding for text"""
         if self.embedding_service:
             # Use real embedding service
-            return await self.embedding_service.get_embedding(text)
+            vector = await self.embedding_service.get_embedding(text)
+            return vector
         else:
             # Fallback to deterministic random embedding for consistency
             np.random.seed(hash(text) % 2**32)
-            return np.random.random(self.embedding_model.get("dimension", 768)).tolist()
+            fallback_dim = self.embedding_model.get("dimension", getattr(self.embedding_service, "dimension", 384) or 384)
+            return np.random.random(fallback_dim).tolist()
     
     async def _generate_embeddings(self, texts: List[str], is_document: bool = True) -> List[List[float]]:
         """Generate embeddings for multiple texts (batch processing)"""
@@ -650,14 +679,91 @@ class RAGModule(BaseModule):
                 prefixed_texts = texts
 
             # Use real embedding service for batch processing
-            return await self.embedding_service.get_embeddings(prefixed_texts)
+            backend = getattr(self.embedding_service, "backend", "unknown")
+            start_time = time.time()
+            logger.info(
+                "Embedding batch requested",
+                extra={
+                    "backend": backend,
+                    "model": getattr(self.embedding_service, "model_name", "unknown"),
+                    "count": len(prefixed_texts),
+                    "scope": "documents" if is_document else "queries"
+                },
+            )
+            embeddings = await self.embedding_service.get_embeddings(prefixed_texts)
+            duration = time.time() - start_time
+            logger.info(
+                "Embedding batch finished",
+                extra={
+                    "backend": backend,
+                    "model": getattr(self.embedding_service, "model_name", "unknown"),
+                    "count": len(embeddings),
+                    "scope": "documents" if is_document else "queries",
+                    "duration_sec": round(duration, 4)
+                },
+            )
+            return embeddings
         else:
             # Fallback to individual processing
+            logger.warning(
+                "Embedding service unavailable, falling back to per-item generation",
+                extra={
+                    "count": len(texts),
+                    "scope": "documents" if is_document else "queries"
+                },
+            )
             embeddings = []
             for text in texts:
                 embedding = await self._generate_embedding(text)
                 embeddings.append(embedding)
             return embeddings
+
+    def _get_collection_vector_size(self, collection_name: Optional[str]) -> int:
+        """Return the expected vector size for a collection, caching results."""
+        default_dim = self.embedding_model.get(
+            "dimension",
+            getattr(self.embedding_service, "dimension", 384) or 384
+        )
+
+        if not collection_name:
+            return default_dim
+
+        if collection_name in self.collection_vector_sizes:
+            return self.collection_vector_sizes[collection_name]
+
+        try:
+            info = self.qdrant_client.get_collection(collection_name)
+            vectors_param = getattr(info.config.params, "vectors", None) if hasattr(info, "config") else None
+            existing_size = None
+            if vectors_param is not None and hasattr(vectors_param, "size"):
+                existing_size = vectors_param.size
+            elif isinstance(vectors_param, dict):
+                existing_size = vectors_param.get("size")
+
+            if existing_size:
+                self.collection_vector_sizes[collection_name] = existing_size
+                return existing_size
+        except Exception as e:
+            logger.debug(f"Unable to determine vector size for {collection_name}: {e}")
+
+        self.collection_vector_sizes[collection_name] = default_dim
+        return default_dim
+
+    def _align_embedding_dimension(self, vector: List[float], collection_name: Optional[str]) -> List[float]:
+        """Pad or truncate embeddings to match the target collection dimension."""
+        if vector is None:
+            return vector
+
+        target_dim = self._get_collection_vector_size(collection_name)
+        current_dim = len(vector)
+
+        if current_dim == target_dim:
+            return vector
+        if current_dim > target_dim:
+            return vector[:target_dim]
+        # Pad with zeros to reach the target dimension
+        padding = [0.0] * (target_dim - current_dim)
+        return vector + padding
     
     def _chunk_text(self, text: str, chunk_size: int = None) -> List[str]:
         """Split text into overlapping chunks for better context preservation"""
@@ -1180,8 +1286,9 @@ class RAGModule(BaseModule):
             # Create document points
             points = []
             for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+                aligned_embedding = self._align_embedding_dimension(embedding, collection_name)
                 chunk_id = str(uuid.uuid4())
-                
+
                 chunk_metadata = {
                     **metadata,
                     "document_id": doc_id,
@@ -1193,7 +1300,7 @@ class RAGModule(BaseModule):
                 
                 points.append(PointStruct(
                     id=chunk_id,
-                    vector=embedding,
+                    vector=aligned_embedding,
                     payload=chunk_metadata
                 ))
             
@@ -1261,8 +1368,9 @@ class RAGModule(BaseModule):
             # Create document points with enhanced metadata
             points = []
             for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+                aligned_embedding = self._align_embedding_dimension(embedding, collection_name)
                 chunk_id = str(uuid.uuid4())
-                
+
                 chunk_metadata = {
                     **processed_doc.metadata,
                     "document_id": processed_doc.id,
@@ -1284,7 +1392,7 @@ class RAGModule(BaseModule):
                 
                 points.append(PointStruct(
                     id=chunk_id,
-                    vector=embedding,
+                    vector=aligned_embedding,
                     payload=chunk_metadata
                 ))
             
@@ -1548,6 +1656,8 @@ class RAGModule(BaseModule):
                 logger.warning(f"sentence-transformers not available, falling back to default embedding for {collection_name}")
                 optimized_query = f"query: {query}"
                 query_embedding = await self._generate_embedding(optimized_query)
+
+            query_embedding = self._align_embedding_dimension(query_embedding, collection_name)
             
             # Build filter
             search_filter = None

frontend/src/components/chatbot/ChatInterface.tsx

@@ -87,6 +87,7 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
   const [messages, setMessages] = useState<ChatMessage[]>([])
   const [input, setInput] = useState("")
   const [isLoading, setIsLoading] = useState(false)
+  const [conversationId, setConversationId] = useState<string | undefined>(undefined)
   const scrollAreaRef = useRef<HTMLDivElement>(null)
   const { success: toastSuccess, error: toastError } = useToast()
 
@@ -103,6 +104,12 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
     scrollToBottom()
   }, [messages])
 
+  useEffect(() => {
+    // Reset conversation when switching chatbots
+    setMessages([])
+    setConversationId(undefined)
+  }, [chatbotId])
+
   const sendMessage = useCallback(async () => {
     if (!input.trim() || isLoading) return
 
@@ -119,12 +126,13 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
     setIsLoading(true)
 
     // Enhanced logging for debugging
+    const currentConversationId = conversationId
     const debugInfo = {
       chatbotId,
       messageLength: messageToSend.length,
-      conversationId,
+      conversationId: currentConversationId ?? null,
       timestamp: new Date().toISOString(),
-      messagesCount: messages.length
+      messagesCount: messages.length + 1
     }
     console.log('=== CHAT REQUEST DEBUG ===', debugInfo)
 
@@ -132,7 +140,7 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
       let data: any
 
       // Use internal API
-      const conversationHistory = messages.map(msg => ({
+      const conversationHistory = [...messages, userMessage].map(msg => ({
         role: msg.role,
         content: msg.content
       }))
@@ -140,7 +148,7 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
       data = await chatbotApi.sendMessage(
         chatbotId,
         messageToSend,
-        undefined, // No conversation ID
+        currentConversationId,
         conversationHistory
       )
 
@@ -154,6 +162,11 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
 
       setMessages(prev => [...prev, assistantMessage])
 
+      const newConversationId = data?.conversation_id || currentConversationId
+      if (newConversationId !== conversationId) {
+        setConversationId(newConversationId)
+      }
+
     } catch (error) {
       const appError = error as AppError
       
@@ -168,7 +181,7 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
     } finally {
       setIsLoading(false)
     }
-  }, [input, isLoading, chatbotId, messages, toastError])
+  }, [input, isLoading, chatbotId, messages, toastError, conversationId])
 
   const handleKeyPress = useCallback((e: React.KeyboardEvent) => {
     if (e.key === 'Enter' && !e.shiftKey) {
@@ -358,4 +371,4 @@ export function ChatInterface({ chatbotId, chatbotName, onClose }: ChatInterface
       </CardContent>
     </Card>
   )
-}
+}